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Sebastian Thrun,
Cezanne Camacho,
Jay Alammar,
Alexis Cook,
Luis Serrano,
Juan Delgado,
and
Ortal Arel
Register for this course and see more details by visiting:
OpenCourser.com/course/hhc504/more
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What's inside
Syllabus
Get a high-level overview of how fully-convolutional neural networks work, and see how they can be used to classify every pixel in an image.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Examines fully-convolutional neural networks, a key technology in image classification
Taught by recognized instructors in the field of deep learning
Requires no prerequisite knowledge, making it accessible to beginners
Provides a hands-on experience with interactive materials
Part of a larger series on deep learning, offering further exploration opportunities
Save this course
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in More Deep Learning Models with these
activities:
Read 'Deep Learning with Python' by François Chollet
Show steps
Gain a solid foundation in deep learning, including convolutional neural networks, by reading a comprehensive book.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Acquire a copy of 'Deep Learning with Python'.
-
Read the chapters related to convolutional neural networks.
-
Work through the exercises and examples provided in the book.
Practice Image Classification with FCNs
Show steps
Solidify your understanding of how FCNs are used for image classification through repetitive exercises.
Browse courses on
Image Classification
Show steps
-
Identify the input and output of an FCN for image classification.
-
Implement an FCN model for a specific image classification task.
-
Evaluate the performance of your model on a dataset.
Watch video tutorials on fully-convolutional neural networks
Show steps
Gain a deeper understanding of how fully-convolutional neural networks work through guided tutorials.
Show steps
-
Find video tutorials on fully-convolutional neural networks.
-
Watch the videos and take notes.
-
Try out the techniques shown in the videos.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Discuss fully-convolutional neural networks with peers
Show steps
Engage in discussions with peers to clarify concepts, share knowledge, and gain different perspectives on fully-convolutional neural networks.
Browse courses on
Convolutional Neural Networks
Show steps
-
Find a study group or online forum where you can connect with peers.
-
Participate in discussions and ask questions.
-
Share your own knowledge and insights.
Complete coding exercises
Show steps
Practice coding fully-convolutional neural networks to solidify understanding and reinforce concepts.
Show steps
-
Find online coding exercises or practice problems.
-
Solve coding exercises on your own.
-
Review solutions and compare your approach.
Explore Applications of FCNs in Object Detection
Show steps
Expand your knowledge of FCNs by following tutorials on their application in object detection.
Browse courses on
Object Detection
Show steps
-
Find tutorials on using FCNs for object detection.
-
Follow the tutorials to implement an FCN for object detection.
-
Test your model on your own dataset.
Build a project using fully-convolutional neural networks
Show steps
Apply your knowledge of fully-convolutional neural networks by building a project that utilizes them.
Browse courses on
Image Classification
Show steps
-
Identify a problem or task that can be solved using fully-convolutional neural networks.
-
Design and implement a fully-convolutional neural network model.
-
Train and evaluate the model.
-
Deploy the model and use it to solve the problem or task.
Participate in a hackathon or competition focused on fully-convolutional neural networks
Show steps
Challenge yourself and showcase your skills by participating in a hackathon or competition centered around fully-convolutional neural networks.
Show steps
-
Find a hackathon or competition that aligns with your interests.
-
Form a team or work individually.
-
Develop a solution using fully-convolutional neural networks.
-
Present your solution and compete for prizes.
Contribute to an open-source project involving fully-convolutional neural networks
Show steps
Make meaningful contributions to the field of fully-convolutional neural networks by participating in an open-source project.
Show steps
-
Identify an open-source project that aligns with your interests and skills.
-
Fork the project and make changes.
-
Submit a pull request and collaborate with other contributors.
Read 'Deep Learning with Python' by François Chollet
Show steps
Gain a solid foundation in deep learning, including convolutional neural networks, by reading a comprehensive book.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Acquire a copy of 'Deep Learning with Python'.
- Read the chapters related to convolutional neural networks.
- Work through the exercises and examples provided in the book.
Practice Image Classification with FCNs
Show steps
Solidify your understanding of how FCNs are used for image classification through repetitive exercises.
Browse courses on
Image Classification
Show steps
Show steps
Show steps
- Identify the input and output of an FCN for image classification.
- Implement an FCN model for a specific image classification task.
- Evaluate the performance of your model on a dataset.
Watch video tutorials on fully-convolutional neural networks
Show steps
Gain a deeper understanding of how fully-convolutional neural networks work through guided tutorials.
Show steps
Show steps
Show steps
- Find video tutorials on fully-convolutional neural networks.
- Watch the videos and take notes.
- Try out the techniques shown in the videos.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Discuss fully-convolutional neural networks with peers
Show steps
Engage in discussions with peers to clarify concepts, share knowledge, and gain different perspectives on fully-convolutional neural networks.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Find a study group or online forum where you can connect with peers.
- Participate in discussions and ask questions.
- Share your own knowledge and insights.
Complete coding exercises
Show steps
Practice coding fully-convolutional neural networks to solidify understanding and reinforce concepts.
Show steps
Show steps
Show steps
- Find online coding exercises or practice problems.
- Solve coding exercises on your own.
- Review solutions and compare your approach.
Explore Applications of FCNs in Object Detection
Show steps
Expand your knowledge of FCNs by following tutorials on their application in object detection.
Browse courses on
Object Detection
Show steps
Show steps
Show steps
- Find tutorials on using FCNs for object detection.
- Follow the tutorials to implement an FCN for object detection.
- Test your model on your own dataset.
Build a project using fully-convolutional neural networks
Show steps
Apply your knowledge of fully-convolutional neural networks by building a project that utilizes them.
Browse courses on
Image Classification
Show steps
Show steps
Show steps
- Identify a problem or task that can be solved using fully-convolutional neural networks.
- Design and implement a fully-convolutional neural network model.
- Train and evaluate the model.
- Deploy the model and use it to solve the problem or task.
Participate in a hackathon or competition focused on fully-convolutional neural networks
Show steps
Challenge yourself and showcase your skills by participating in a hackathon or competition centered around fully-convolutional neural networks.
Show steps
Show steps
Show steps
- Find a hackathon or competition that aligns with your interests.
- Form a team or work individually.
- Develop a solution using fully-convolutional neural networks.
- Present your solution and compete for prizes.
Contribute to an open-source project involving fully-convolutional neural networks
Show steps
Make meaningful contributions to the field of fully-convolutional neural networks by participating in an open-source project.
Show steps
Show steps
Show steps
- Identify an open-source project that aligns with your interests and skills.
- Fork the project and make changes.
- Submit a pull request and collaborate with other contributors.
Career center
Learners who complete More Deep Learning Models will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for developing and implementing machine learning models. They work with data scientists to identify the right machine learning algorithms to use for a given problem, and then they build and tune the models to achieve the desired results. This course can help Machine Learning Engineers to develop the skills they need to build and implement fully-convolutional neural networks, which are a powerful type of deep learning model that can be used for a variety of tasks, such as image classification and object detection.
Data Scientist
Data Scientist
Data Scientists use their knowledge of statistics, mathematics, and computer science to extract insights from data. They work with businesses to identify the data that is most relevant to their needs, and then they develop machine learning models to analyze the data and make predictions. This course can help Data Scientists to develop the skills they need to build and implement fully-convolutional neural networks, which are a powerful type of deep learning model that can be used for a variety of tasks, such as image classification and object detection.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers develop and implement computer vision systems. These systems use cameras and other sensors to capture images and videos, and then they use machine learning algorithms to analyze the images and videos to extract insights. This course can help Computer Vision Engineers to develop the skills they need to build and implement fully-convolutional neural networks, which are a powerful type of deep learning model that is well-suited for computer vision tasks.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They work with businesses to understand their needs, and then they design and develop software solutions that meet those needs. This course can help Software Engineers to develop the skills they need to build and implement fully-convolutional neural networks, which are a powerful type of deep learning model that can be used for a variety of tasks, such as image classification and object detection.
Research Scientist
Research Scientist
Research Scientists conduct research in a variety of fields, including computer science, mathematics, and statistics. They develop new theories and algorithms, and they apply these theories and algorithms to solve real-world problems. This course can help Research Scientists to develop the skills they need to build and implement fully-convolutional neural networks, which are a powerful type of deep learning model that can be used for a variety of tasks, such as image classification and object detection.
Data Analyst
Data Analyst
Data Analysts use their knowledge of statistics and computer science to analyze data and extract insights. They work with businesses to identify the data that is most relevant to their needs, and then they develop reports and visualizations that communicate the insights to decision-makers. This course can help Data Analysts to develop the skills they need to build and implement fully-convolutional neural networks, which are a powerful type of deep learning model that can be used for a variety of tasks, such as image classification and object detection.
Business Analyst
Business Analyst
Business Analysts work with businesses to identify their needs and develop solutions to meet those needs. They use their knowledge of business processes and technology to develop solutions that are both effective and efficient. This course can help Business Analysts to develop the skills they need to build and implement fully-convolutional neural networks, which are a powerful type of deep learning model that can be used for a variety of tasks, such as image classification and object detection.
Product Manager
Product Manager
Product Managers are responsible for developing and managing products. They work with engineers, designers, and marketers to bring products to market that meet the needs of customers. This course can help Product Managers to develop the skills they need to build and implement fully-convolutional neural networks, which are a powerful type of deep learning model that can be used for a variety of tasks, such as image classification and object detection.
Quality Assurance Tester
Quality Assurance Tester
Quality Assurance Testers are responsible for testing software to ensure that it is free of defects. They work with developers to identify and fix bugs. This course can help Quality Assurance Testers to develop the skills they need to build and implement fully-convolutional neural networks, which are a powerful type of deep learning model that can be used for a variety of tasks, such as image classification and object detection.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and implementing marketing campaigns. They work with businesses to identify their target audience and develop marketing campaigns that reach that audience. This course can help Marketing Managers to develop the skills they need to build and implement fully-convolutional neural networks, which are a powerful type of deep learning model that can be used for a variety of tasks, such as image classification and object detection.
Project Manager
Project Manager
Project Managers are responsible for planning and managing projects. They work with teams to ensure that projects are completed on time and within budget. This course can help Project Managers to develop the skills they need to build and implement fully-convolutional neural networks, which are a powerful type of deep learning model that can be used for a variety of tasks, such as image classification and object detection.
Sales Manager
Sales Manager
Sales Managers are responsible for developing and implementing sales strategies. They work with sales teams to identify and close deals. This course can help Sales Managers to develop the skills they need to build and implement fully-convolutional neural networks, which are a powerful type of deep learning model that can be used for a variety of tasks, such as image classification and object detection.
Financial Analyst
Financial Analyst
Financial Analysts use their knowledge of finance and economics to analyze financial data and make investment recommendations. This course can help Financial Analysts to develop the skills they need to build and implement fully-convolutional neural networks, which are a powerful type of deep learning model that can be used for a variety of tasks, such as image classification and object detection.
Operations Manager
Operations Manager
Operations Managers are responsible for planning and managing the day-to-day operations of a business. They work with employees to ensure that the business is running smoothly and efficiently. This course can help Operations Managers to develop the skills they need to build and implement fully-convolutional neural networks, which are a powerful type of deep learning model that can be used for a variety of tasks, such as image classification and object detection.
Human Resources Manager
Human Resources Manager
Human Resources Managers are responsible for managing the human resources of a business. They work with employees to recruit, hire, and train new employees. This course can help Human Resources Managers to develop the skills they need to build and implement fully-convolutional neural networks, which are a powerful type of deep learning model that can be used for a variety of tasks, such as image classification and object detection.
For more career information including salaries, visit:
OpenCourser.com/course/hhc504/more
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
More Deep Learning Models.
Save
Provides a comprehensive overview of deep learning, covering the latest advances in the field. It is an excellent resource for learners who want to gain a deeper understanding of the theoretical foundations of deep learning.
Save
Provides a comprehensive overview of computer vision, including topics such as image processing, feature extraction, and object recognition. It valuable resource for learners who want to gain a broader understanding of the field.
Provides a comprehensive overview of pattern recognition and machine learning, including topics such as supervised and unsupervised learning, dimensionality reduction, and model selection. It valuable resource for learners who want to gain a deeper understanding of the theoretical foundations of machine learning.
Provides a comprehensive overview of deep learning for natural language processing, covering topics such as word embeddings, sequence models, and attention mechanisms. It valuable resource for learners who want to gain a deeper understanding of this field.
Provides a practical guide to natural language processing with PyTorch, covering topics such as data preprocessing, feature engineering, model selection, and evaluation. It valuable resource for learners who want to gain hands-on experience with natural language processing.
Provides a comprehensive overview of speech and language processing, covering topics such as speech recognition, natural language understanding, and machine translation. It valuable resource for learners who want to gain a deeper understanding of this field.
Provides a practical guide to deep learning with Python, covering topics such as data preprocessing, feature engineering, model selection, and evaluation. It valuable resource for learners who want to gain hands-on experience with deep learning.
Provides a practical guide to TensorFlow for deep learning, covering topics such as data preprocessing, feature engineering, model selection, and evaluation. It valuable resource for learners who want to gain hands-on experience with TensorFlow.
Provides a practical guide to deep learning with R, covering topics such as data preprocessing, feature engineering, model selection, and evaluation. It valuable resource for learners who want to gain hands-on experience with deep learning in R.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/hhc504/more
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Time
60 hours
Level
Advanced
Via
Udacity
Instructors
Sebastian Thrun
Cezanne Camacho
Jay Alammar
Alexis Cook
Luis Serrano
Juan Delgado
Ortal Arel
Language
English
Good to know
Examines fully-convolutional neural networks, a key technology in image classification
Taught by recognized instructors in the field of deep learning
Requires no prerequisite knowledge, making it accessible to beginners
Provides a hands-on experience with interactive materials
Part of a larger series on deep learning, offering further exploration opportunities
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